The proliferation of computer systems and improvements in telecommunications makes an overwhelming amount of information available to a computer user. Massive networks such as the Internet provide millions of data items in the form of words, numbers, images, etc., in very diverse and unregulated formats. Other, smaller information systems, such as intranets and stand-alone computer systems, are more restrictive in their data formats yet still provide large volumes of information to the user. Perhaps the smallest application of a computerized information system is with today's so-called personal digital assistants (PDAs), which may contain an individual's address book, calendar or similar personal information.
Within the range of all of these information systems lie the same basic problems of efficient access to, and analysis of, the information. Typical user applications are designed primarily to provide ease of entry, upkeep and retrieval. However, the applications require that the information system be specifically designed for a target application, e.g., medical record-keeping, so that "records," "templates," or similar structures must be designed by a programmer or architect in order for the application to be usefull to an end user.
More recently, popular search engines have been created which allow users to search larger, less-structured information systems such as the Internet with relational query operators. For example, some Internet search engines allow relational queries using keywords that do not relate to specific fields. Instead, any documents having words with a specified relationship, such as a keyword matching relationship, are listed as possible documents of interest to the user.
FIG. 4 shows a prior art screen display of a search result 400 derived from a keyword search conducted over the Internet. As can be seen, the search result 400 comprises a series of document citations such as shown at 402. Citations in the search result 400 are displayed in order of decreasing confidence factors 405. The confidence factors represent how well the documents satisfied the search criteria. For example, if three search terms were used to form the search criteria, a document that satisfies all three search terms would have a confidence factor of 100%. If only two of three search terms were satisfied, the confidence factor would be 66%. In FIG. 4 as shown at 404, the search engine found 92 documents having one or more of the keyword search terms. As can be seen, the documents have a document number 410 which ranks the documents by their associated confidence factors. Of the 92 documents, only 8 citations are displayed on the screen at any one time due to the constraints of the display system. When a larger display or a smaller character font is used, more than 8 citations may be displayed, however, all 92 citations would generally not fit onto one display screen. Printing out the 92 citations on a standard printer may require several pages of print out. As a result, it becomes difficult to analyze large search results because of the limitations inherent in current display techniques.
Another use for information systems is to provide a platform for analyzing data to determine characteristics, trends or predictive guidelines in the information. For example, when financial data is being analyzed it may be useful to discover that where inflation is high in an overseas market, bond prices in a different market are also correspondingly high. Or, in a medical research application, it would be useful to determine that in a large percentage of cases where a certain treatment was used the recovery time was very short. However, such analysis of data is very difficult with traditional search displays, such as typically used on the Internet, which singularly focus on retrieving all existing information that match a simple query and displaying the result without regard for how the user may want to interpret it.
As discussed above, query searches as described, when performed on a large information system such as the Internet, may result in the retrieval of hundreds of documents. Because of the limitations in current display techniques, the job is then left to the user to filter through this large result to find documents of interest. Therefore, unless the user knows with high specificity the type of information sought, and can form very specific search criteria, a large and virtually unusable search result can be created.
Thus, it is desirable to have a technique and system for analyzing characteristics of information in the manner discussed above. Further, it is desirable to have such a technique and system that is usable with search results regardless of the size or level of structuring. Also, given the vast amount of information available, it is vital that the results of the analysis system be presented in a form that is efficient for detecting trends, qualities or other useful relationship among the information being analyzed.